arXiv Machine Learning

Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR

The paper introduces Contrastive Uncertainty Learning (CUL), a supervised framework that leverages voxel‑wise predictive uncertainty to guide representation learning for multi‑structure segmentation in lower‑pelvic MRI. Using Monte Carlo Dropout, CUL identifies uncertain voxels and shapes the embedding space so that low‑uncertainty, correctly predicted features become stable while high‑uncertainty or misclassified features are repelled, preventing feature collapse at challenging boundaries. Evaluated on 589 prostate MRI scans with a ViT‑based UperNet decoder, CUL achieves comparable Dice scores to a baseline while significantly reducing surface distance for most structures, and ablation studies confirm the necessity of both uncertainty‑guided sampling and the contrastive objective.

arXiv Machine Learning

Sep 14

Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation

The paper introduces RABR-Net, a two‑stage framework that refines biomedical image segmentation boundaries by combining multiple uncertainty measures into a boundary‑aware representation. A gated residual refiner uses this representation to selectively correct uncertain boundary pixels while preserving confident regions, leading to modest but statistically significant improvements in Dice, Boundary Dice, and HD95 metrics on a held‑out test set. Qualitative results show the refiner focuses on uncertain cytoplasm and nucleus boundaries, though calibration does not automatically improve.

By Anima Kujur

computer-vision

arXiv Machine Learning

Jun 18

Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation

arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.

By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar

computer-vision safety

arXiv AI

Jul 14

SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

arXiv:2604. 15271v3 Announce Type: replace-cross Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support.

By Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Yucheng Chen

computer-vision

arXiv Computer Vision

Sep 14

Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation

arXiv:2609.06729v2 Announce Type: replace Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning....

By Libing Kuang, Soren Salehi, Ziling Wu, Ahmad P. Tafti, Armaghan Moemeni

computer-vision efficiency multimodal benchmarks data-engineering

arXiv AI

Jul 8

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

arXiv:2603. 04024v2 Announce Type: replace-cross Abstract: Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible.

By Chao Wu, Mahesh Bhosale, Kangxian Xie, Pouya Karimian, David Doermann, Mingchen Gao

diffusion computer-vision safety

arXiv Computer Vision

Oct 2

Uncertainty-Guided Handshake: Efficient Human-in-the-Loop Refinement for Surgical-Grade Glioma Segmentation

arXiv:2610.01452v1 Announce Type: new Abstract: While state-of-the-art automated models for medical image segmentation achieve high mean performance, they frequently suffer from localized, catastroph...

By Samuel Hart, Ahmad Yahya, Ahmed Karam Eldaly

computer-vision benchmarks safety

arXiv Machine Learning

Jul 28

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.

By Pranav Kaliaperumal, Manisha Kaliaperumal

computer-vision multimodal benchmarks safety

arXiv Computer Vision

Sep 4

Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial

The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.

By Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway

computer-vision

arXiv AI

Jun 3

ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements

arXiv:2606. 03069v1 Announce Type: cross Abstract: Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across multiple domains.

By Aqsa Naseer, Maryam Bibi, Syeda Samiya Urooj, Muhammad Khurram Shahzad

computer-vision efficiency benchmarks

arXiv Machine Learning

Aug 18

Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation

arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.

By Simon Baur, Arne Schernich, Ekin B\"oke, Wojciech Samek, Jackie Ma

computer-vision safety

arXiv Computer Vision

4d ago

DART: Scaling a Shape Representation Across Anatomical Classes and Imaging Modalities

arXiv:2610.27605v1 Announce Type: new Abstract: Current state-of-the-art segmentation models still produce anatomically implausible shapes, such as broken vessels and fragmented glands, on the struct...

By Koushik Swarna, Pedro R. A. S. Bassi, Lam Ngo, Wenxuan Li, Jiancheng Yang, Alan Yuille, Zongwei Zhou

diffusion computer-vision benchmarks

arXiv Machine Learning

Jun 16

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.

By Jimut B. Pal, Suyash P. Awate

computer-vision

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